Top Machine Learning Jobs in Paris

The Strategic Sales Executive will sell Identity Governance Administration solutions to multinational organizations, engaging with senior decision-makers and navigating lengthy sales cycles.

Applied AI Interns will research and benchmark AI models, develop scalable generative AI and machine learning features, and deploy, monitor, and maintain production AI systems. They will analyze large-scale observability data, investigate emerging model architectures, present research, and collaborate with engineering and product teams to deliver customer-facing capabilities.

Advise Mirakl’s legal function on AI, emerging technology, privacy, data protection, regulatory compliance, and intellectual property. Design and deploy AI agents and automation for legal workflows, establish responsible AI policies, lead legal operations transformation, optimize matter management and vendor processes, and train legal, compliance, and business teams on AI-related legal topics.

Leads analytical methodology and evaluation for AI-based healthcare and pharmaceutical workflows. Owns model selection, cohort and HCP segmentation, agent decision logic, and evaluation frameworks. Partners with engineering, data architecture, and governance teams to productionize rigorous analytical methods, identify new data and techniques, communicate insights, and mentor data science and engineering colleagues.

Leads data science methodology and advanced analytics for AI-enabled Medical Affairs workflows. Develops and evaluates statistical, machine-learning, NLP, and AI models using healthcare and real-world data; defines validation, monitoring, and decision frameworks; partners with engineering, governance, and business teams to operationalize solutions. Provides technical leadership, mentorship, documentation standards, and strategic guidance while ensuring privacy, compliance, responsible AI use, and clear communication of analytical findings.

Leads ad hoc and exploratory analyses for medical and business questions across a global Medical Insights organization. Prepares and validates datasets, translates ambiguous stakeholder needs into decision-ready findings, and maintains analytical documentation. Tests new data sources, methods, and AI-enabled approaches through proof-of-concept analyses to guide platform investments and roadmap decisions. Partners with business and product stakeholders while supporting compliant, reproducible use of regulated data.

Lead product development for Datadog APM features, focusing on distributed tracing, performance analysis, and AI-driven troubleshooting. Work with engineers, designers, and customers to validate problems, deliver prototypes to production, define go-to-market, and measure adoption and impact.

Provides staff-level technical leadership for enterprise AI/ML platform infrastructure. Designs and operates AWS, Kubernetes, serverless, and containerized environments; develops infrastructure-as-code, CI/CD, observability, security, governance, and cost-optimization standards. Supports generative AI workloads, model routing, reliability, scalability, and performance. Partners with engineering, security, and operations teams, mentors engineers, reviews architecture, and drives platform maturity across multiple teams.

Lead end-to-end ML product roadmaps and initiatives (e.g., Attachment Matcher, Transaction Categorizer). Collaborate with data scientists, engineers, and stakeholders to define, deliver, and monitor ML solutions that drive measurable business value for SMEs across Qonto's platform.

Operate and integrate AI systems including RAG pipelines, copilots, agentic workflows, and predictive ML services. Design AI observability and monitoring frameworks, manage performance and reliability, troubleshoot production issues, define SLAs and SLOs, and optimize cloud consumption and inference costs. Collaborate with engineering, product, and business stakeholders to balance performance, quality, and cost while supporting incident management and continuous improvement.

As a Senior Applied Scientist, you will design machine learning solutions, develop scalable features, and analyze data within the Datadog app, collaborating with engineering and product teams.

As an AI Researcher at Datadog, you'll conduct cutting-edge research in AI, focusing on foundation models and AI agents for observability and reliability, collaborating with cross-functional teams, and contributing to scientific publications.

Lead product strategy and roadmap for AI and data security features—AI discovery, posture management, and data protection. Collaborate with engineering, design, security research, and GTM teams to define, build, and launch capabilities. Engage customers to validate solutions and drive adoption while translating security research into actionable product features.

Lead pharmacovigilance domain expertise for Veeva Falcon by guiding safety case workflows, evaluating AI-driven outputs, shaping PV automation strategy, engaging early adopters, and designing complex stress-test scenarios. The role bridges hands-on drug safety operations with product and engineering teams to improve platform accuracy, compliance, and customer value.

Develop RF and radar signal-processing algorithms for detecting, characterizing, classifying, and identifying emitters in contested environments. Build electronic order-of-battle analytics, apply machine learning to modulation recognition and anomaly detection, fuse Radar, Radio, and Electronic Warfare data, and transition prototypes from MATLAB/Python into real-time implementations with software and embedded/FPGA teams. Validate performance using simulated and real-world datasets in laboratory and field conditions.

Develop and optimize real-time algorithms for 3D LiDAR perception, including point-cloud clustering, classification, semantic segmentation, and ground detection. Responsibilities include researching solutions, designing algorithmic improvements, creating evaluation datasets, integrating modules into C++ software, conducting unit and system testing, characterizing performance, and documenting results. The role requires machine learning and deep learning expertise, independent ownership, collaboration, and fluent English.

Develop and implement machine learning and deep learning algorithms to analyze MRI data, collaborate with neuroscientists and clinicians to define model requirements, evaluate and optimize models for clinical applications, and write reports and scientific publications.

Lead operations consulting engagements across sourcing, procurement, supply chain, manufacturing, process operations, and digital operations. Break down complex problems, direct junior consultants, conduct sophisticated analyses, develop recommendations, collaborate with clients, and manage small project teams. Candidates should bring 4–8 years of relevant experience, sector expertise, strong analytical and communication skills, fluency in English and French, and willingness to travel.

Designs and leads enterprise AI transformations for complex Workday customers. Advises HR, Finance, and IT executives; defines AI-enabled processes, operating models, use cases, roadmaps, and adoption plans. Leads functional workstreams covering requirements, process design, configuration, data readiness, governance, and user experience. Partners with Product, Engineering, and technical teams on AI agents, integrations, security, and data models while influencing platform evolution and ensuring ethical, scalable, measurable solutions.

Design, prototype, evaluate, and ship AI agents and reusable skills for product and analytical challenges. Build agent evaluators, guardrails, golden datasets, retrieval systems, and LLM-powered diagnostics. Collaborate with Product, R&D, GTM, and Design teams to frame ambiguous problems, analyze large datasets, develop predictive models, and create actionable insights. The internship requires a builder mindset, strong analytical ability, and proficiency in Python, SQL, data pipelines, and visualization.

Own computer vision and generative AI features from research and prototyping through production. Evaluate models, build scalable and cost-efficient inference pipelines, optimize performance, and integrate AI into customer-facing products. The role includes technical documentation, benchmarking, mentoring, knowledge sharing, and technical leadership while remaining primarily hands-on. Work focuses on image and video generation, perception models, production deployment, monitoring, and continuous improvement.

Leads the strategy, roadmap, and adoption of data products, including AI-enabled capabilities. Identifies business opportunities for data, partners with engineering and Data Science teams to develop intelligent products, and manages delivery, quality, metrics, budgets, and stakeholder communications. Oversees Product Owners, leads steering committees, and works with business and technical leaders to maximize product value.

Own the vision, roadmap, backlog, and delivery of AI initiatives across ARCHIMED. Translate business needs into product requirements, coordinate technical and business stakeholders, drive adoption and change management, evaluate AI tools and vendors, and support responsible AI governance. Monitor KPIs, manage risks, develop support materials, and ensure AI solutions deliver measurable business value.

Leads Criteo’s AI Lab and a team of 70+ researchers and engineers. Defines the company’s long-term AI vision, advances deep learning and generative AI, translates research into production systems, influences product strategy, and represents Criteo externally. Requires extensive leadership of AI organizations, strong research credibility, production AI experience, and expertise in modern machine learning and large-scale AI systems.

Designs, builds, and operates production-grade agentic AI systems and orchestration frameworks. Responsibilities include prompt architecture, tool and API integrations, monitoring, evaluation, cost controls, reliability improvements, and governance documentation. The engineer collaborates with data science, data engineering, and governance teams to ensure reliable, compliant workflows using structured healthcare and pharmaceutical data.